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https://issues.apache.org/jira/browse/MAHOUT-165?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12762167#action_12762167
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Shashikant Kore commented on MAHOUT-165:
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I am trying out this patch. Somehow, I find it extremely slow compared to colt.
I am running KMeans on 100k test vectors. With colt, all 5 iterations and
clustering finished in 8 minutes. With this patch, it's been an hour and it
hasn't even completed 50% of Iteration 1. (Iteration 0 was completed in 3
minutes.) I checked kmeans.Cluster.java and verified that correct method on
distance measure is called (one with 3 parameters). It is correct, and it can
be verified by the quick completion of Iteration 0.
I am not not able to understand this behaviour.
> Using better primitives hash for sparse vector for performance gains
> --------------------------------------------------------------------
>
> Key: MAHOUT-165
> URL: https://issues.apache.org/jira/browse/MAHOUT-165
> Project: Mahout
> Issue Type: Improvement
> Components: Matrix
> Affects Versions: 0.2
> Reporter: Shashikant Kore
> Assignee: Grant Ingersoll
> Fix For: 0.2
>
> Attachments: colt.jar, mahout-165-trove.patch,
> MAHOUT-165-updated.patch, mahout-165.patch, MAHOUT-165.patch, mahout-165.patch
>
>
> In SparseVector, we need primitives hash map for index and values. The
> present implementation of this hash map is not as efficient as some of the
> other implementations in non-Apache projects.
> In an experiment, I found that, for get/set operations, the primitive hash of
> Colt performance an order of magnitude better than OrderedIntDoubleMapping.
> For iteration it is 2x slower, though.
> Using Colt in Sparsevector improved performance of canopy generation. For an
> experimental dataset, the current implementation takes 50 minutes. Using
> Colt, reduces this duration to 19-20 minutes. That's 60% reduction in the
> delay.
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